---
license: gemma
language:
- en
datasets:
- Sao10K/c2-Logs-Filtered
---

GGUF quants of gghfez/gemma-2-27b-rp-c2-v2
Finetune of the gemma2-27b base model.

All quants have FP16 input tensors + output weights. I found quantizing these degraded the quality significantly.

gemma-2-27b-rp-c2-v2.IQ4_XSl.gguf - fits into 16GB VRAM with 16k context

Changes since V1:

- Filtered junk out of the dataset
- prepended <bos> to chatml template (so called gemma_chatml)

I've been using the I14_XSl quant with SillyTavern. The latest SillyTavern has a 'gemma2' template which matches the training, but chatml works fine for me.

Seems to work pretty well with SillyTavern

# Prompting

Model has been Instruct tuned with the Gemma_ChatML formatting. A typical input would look like this:


<bos><|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant


# Training:

Trained on a subset of the synthetic RP dataset from:
Sao10K/c2-Logs-Filtered